Event recounting refers to the attribution of key pieces of evidence supporting the recognition decision. As with most video analytics datasets, only video-level annotations are provided, making such attribution a challenging task. Event recounting is usually implemented as a post-processing step after the recognition [1]}, [2]}. Chang et al. [3]} proposed a joint optimization framework with mid-level semantic concept representations for event recognition and recounting. Sun et al. [4]} introduced an evidence localization model learned via a max-margin framework, and Lai et al. [5]} applied Multiple-Instance Learning (MIL) which infers temporal instance labels and the video-level labels. In these works, event videos are treated as sets of shots or instances, these recounting procedures perform only temporal localization and usually at a coarse scale. Recently, Gan et al. [6]} proposed a deep neural network for event recognition. Specifically, event recounting (both temporal and spatial) is also achieved via passing the classification scores backward. However, this is still an explanatory post-processing step which is never designed to assist the event recognition task.
